I have a sample code in R as follows:
library(igraph)
rm(list=ls())
dat=read.csv(file.choose(),header=TRUE,row.names=1,check.names=T) # read .csv file
m=as.matrix(dat)
net=graph.adjacency(adjmatrix=m,mode="undirected",weighted=TRUE,diag=FALSE)
where I used csv file as input which contain following data:
23732 23778 23824 23871 58009 58098 58256
23732 0 8 0 1 0 10 0
23778 8 0 1 15 0 1 0
23824 0 1 0 0 0 0 0
23871 1 15 0 0 1 5 0
58009 0 0 0 1 0 7 0
58098 10 1 0 5 7 0 1
58256 0 0 0 0 0 1 0
After this I used following command to check weight values:
E(net)$weight
Expected output is somewhat like this:
> E(net)$weight
[1] 8 1 10 1 15 1 1 5 7 1
But I'm getting weird values (and every time different):
> E(net)$weight
[1] 2.121996e-314 2.121996e-313 1.697597e-313 1.291034e-57 1.273197e-312 5.092790e-313 2.121996e-314 2.121996e-314 6.320627e-316 2.121996e-314 1.273197e-312 2.121996e-313
[13] 8.026755e-316 9.734900e-72 1.273197e-312 8.027076e-316 6.320491e-316 8.190221e-316 5.092790e-313 1.968065e-62 6.358638e-316
I'm unable to find where and what I am doing wrong?
Please help me to get the correct expected result and also please tell me why is this weird output and that too every time different when I run it.??
Thanks,
Nitin
Just a small working example below, much clearer than CSV input.
library('igraph');
adjm1<-matrix(sample(0:1,100,replace=TRUE,prob=c(0.9,01)),nc=10);
g1<-graph.adjacency(adjm1);
plot(g1)
P.s. ?graph.adjacency has a lot of good examples (remember to run library('igraph')).
Related threads
Creating co-occurrence matrix
Co-occurrence matrix using SAC?
The problem seems to be due to the data-type of the matrix elements. graph.adjacency expects elements of type numeric. Not sure if its a bug.
After you do,
m <- as.matrix(dat)
set its mode to numeric by:
mode(m) <- "numeric"
And then do:
net <- graph.adjacency(m, mode = "undirected", weighted = TRUE, diag = FALSE)
> E(net)$weight
[1] 8 1 10 1 15 1 1 5 7 1
Related
I am very new to R, so I apologise if this looks simple to someone.
I try to to join two files and then perform a one-sided Fisher's exact test to determine if there is a greater burden of qualifying variants in casefile or controlfile.
casefile:
GENE CASE_COUNT_HET CASE_COUNT_CH CASE_COUNT_HOM CASE_TOTAL_AC
ENSG00000124209 1 0 0 1
ENSG00000064703 1 1 0 9
ENSG00000171408 1 0 0 1
ENSG00000110514 1 1 1 12
ENSG00000247077 1 1 1 7
controlfile:
GENE CASE_COUNT_HET CASE_COUNT_CH CASE_COUNT_HOM CASE_TOTAL_AC
ENSG00000124209 1 0 0 1
ENSG00000064703 1 1 0 9
ENSG00000171408 1 0 0 1
ENSG00000110514 1 1 1 12
ENSG00000247077 1 1 1 7
ENSG00000174776 1 1 0 2
ENSG00000076864 1 0 1 13
ENSG00000086015 1 0 1 25
I have this script:
#!/usr/bin/env Rscript
library("argparse")
suppressPackageStartupMessages(library("argparse"))
parser <- ArgumentParser()
parser$add_argument("--casefile", action="store")
parser$add_argument("--casesize", action="store", type="integer")
parser$add_argument("--controlfile", action="store")
parser$add_argument("--controlsize", action="store", type="integer")
parser$add_argument("--outfile", action="store")
args <- parser$parse_args()
case.dat<-read.delim(args$casefile, header=T, stringsAsFactors=F, sep="\t")
names(case.dat)[1]<-"GENE"
control.dat<-read.delim(args$controlfile, header=T, stringsAsFactors=F, sep="\t")
names(control.dat)[1]<-"GENE"
dat<-merge(case.dat, control.dat, by="GENE", all.x=T, all.y=T)
dat[is.na(dat)]<-0
dat$P_DOM<-0
dat$P_REC<-0
for(i in 1:nrow(dat)){
#Dominant model
case_count<-dat[i,]$CASE_COUNT_HET+dat[i,]$CASE_COUNT_HOM
control_count<-dat[i,]$CONTROL_COUNT_HET+dat[i,]$CONTROL_COUNT_HOM
if(case_count>args$casesize){
case_count<-args$casesize
}else if(case_count<0){
case_count<-0
}
if(control_count>args$controlsize){
control_count<-args$controlsize
}else if(control_count<0){
control_count<-0
}
mat<-cbind(c(case_count, (args$casesize-case_count)), c(control_count, (args$controlsize-control_count)))
dat[i,]$P_DOM<-fisher.test(mat, alternative="greater")$p.value
and problem starts in here:
case_count<-dat[i,]$CASE_COUNT_HET+dat[i,]$CASE_COUNT_HOM
control_count<-dat[i,]$CONTROL_COUNT_HET+dat[i,]$CONTROL_COUNT_HOM
the result of case_count and control_count is NULL values, however corresponding columns in both input files are NOT empty.
I tried to run the script above with assigning absolute numbers (1000 and 2000) to variables case_count and control_count , and the script worked without issues.
The main purpose of the code:
https://github.com/mhguo1/TRAPD
Run burden testing This script will run the actual burden testing. It
performs a one-sided Fisher's exact test to determine if there is a
greater burden of qualifying variants in cases as compared to controls
for each gene. It will perform this burden testing under a dominant
and a recessive model.
It requires R; the script was tested using R v3.1, but any version of
R should work. The script should be run as: Rscript burden.R
--casefile casecounts.txt --casesize 100 --controlfile controlcounts.txt --controlsize 60000 --output burden.out.txt
The script has 5 required options:
--casefile: Path to the counts file for the cases, as generated in Step 2A
--casesize: Number of cases that were tested in Step 2A
--controlfile: Path to the counts file for the controls, as generated in Step 2B
--controlsize: Number of controls that were tested in Step 2B. If using ExAC or gnomAD, please refer to the respective documentation for
total sample size
--output: Output file path/name Output: A tab delimited file with 10 columns:
#GENE: Gene name CASE_COUNT_HET: Number of cases carrying heterozygous qualifying variants in a given gene CASE_COUNT_CH: Number of cases
carrying potentially compound heterozygous qualifying variants in a
given gene CASE_COUNT_HOM: Number of cases carrying homozygous
qualifying variants in a given gene. CASE_TOTAL_AC: Total AC for a
given gene. CONTROL_COUNT_HET: Approximate number of controls carrying
heterozygous qualifying variants in a given gene CONTROL_COUNT_HOM:
Number of controlss carrying homozygous qualifying variants in a given
gene. CONTROL_TOTAL_AC: Total AC for a given gene. P_DOM: p-value
under the dominant model. P_REC: p-value under the recessive model.
I try to run genetic variant burden test with vcf files and external gnomAD controls. I found this repo suitable and trying to fix bugs now in it.
as a newbie in R statistics, I will be happy about any suggestion. Thank you!
If you want all row in two file. You can use full join with by = "GENE" and suffix as you wish
library(dplyr)
z <- outer_join(case_file, control_file, by = "GENE", suffix = c(".CASE", ".CONTROL"))
GENE CASE_COUNT_HET.CASE CASE_COUNT_CH.CASE CASE_COUNT_HOM.CASE CASE_TOTAL_AC.CASE
1 ENSG00000124209 1 0 0 1
2 ENSG00000064703 1 1 0 9
3 ENSG00000171408 1 0 0 1
4 ENSG00000110514 1 1 1 12
5 ENSG00000247077 1 1 1 7
6 ENSG00000174776 NA NA NA NA
7 ENSG00000076864 NA NA NA NA
8 ENSG00000086015 NA NA NA NA
CASE_COUNT_HET.CONTROL CASE_COUNT_CH.CONTROL CASE_COUNT_HOM.CONTROL CASE_TOTAL_AC.CONTROL
1 1 0 0 1
2 1 1 0 9
3 1 0 0 1
4 1 1 1 12
5 1 1 1 7
6 1 1 0 2
7 1 0 1 13
8 1 0 1 25
If you want only GENE that are in both rows, use inner_join
z <- inner_join(case_file, control_file, by = "GENE", suffix = c(".CASE", ".CONTROL"))
GENE CASE_COUNT_HET.CASE CASE_COUNT_CH.CASE CASE_COUNT_HOM.CASE CASE_TOTAL_AC.CASE
1 ENSG00000124209 1 0 0 1
2 ENSG00000064703 1 1 0 9
3 ENSG00000171408 1 0 0 1
4 ENSG00000110514 1 1 1 12
5 ENSG00000247077 1 1 1 7
CASE_COUNT_HET.CONTROL CASE_COUNT_CH.CONTROL CASE_COUNT_HOM.CONTROL CASE_TOTAL_AC.CONTROL
1 1 0 0 1
2 1 1 0 9
3 1 0 0 1
4 1 1 1 12
5 1 1 1 7
For my previous lines of code for making tables from column names, they successfully made short and dense matrices for me to readily process data from two questions (from survey results): (2nd example).
However, when I try using the same line of code (above), I don't get that sleek matrix. I end up getting a list of un-linked tables, which I do not want. Perhaps it's due to the new column only having 0's and 1's as numeric characters, vs. the others that have more than 2: (1st example).
[Please forgive my formatting issues (StackOverflow Status: Newbie). Also, many thanks in advance to those checking in on and answering my question!]
>table(select(data_final, `Relationship 2Affected Individual`, Satisfied_Treatments))
Relationship 2Affected Individual 1
1 0
2 0
3 0
6 0
Other (please specify) 0
, , 1 = 1, Response = 10679308122
0
Relationship 2Affected Individual 1
1 0
2 0
3 0
6 0
Other (please specify) 0
, ,
...
> table(select(data_final, `Relationship 2Affected Individual`, Indirect_Benefits))
Indirect_Benefits
Relationship 2Affected Individual 0 1 2 3
1 4 1 0 0
2 42 17 9 3
3 12 1 1 0
6 5 2 2 0
Other (please specify) 1 0 0 0
>#rstudioapi::versionInfo()
>#packageVersion("dplyr")
table(data_final$Relationship 2Affected Individual, data_final$Satisfied_Treatments)
Problem Solved^
I'm trying to draw contrasts. I've used the following script
contrast <- makeContrasts(tam=("GSM151013-GSM150949"), ("GSM151014-GSM150950"), ("GSM151016-GSM150951"), ("GSM151019-GSM150953"), ("GSM151025-GSM150954"),levels=design)
'design' is as follows
tam tamless
GSM151013 0 1
GSM151014 0 1
GSM151016 0 1
GSM151019 0 1
GSM151025 0 1
GSM150949 1 0
GSM150950 1 0
GSM150951 1 0
GSM150953 1 0
GSM150954 1 0
attr(,"assign")
[1] 1 1
attr(,"contrasts")
attr(,"contrasts")$f
[1] "contr.treatment"
when I run the code I receive the following error:
Error in eval(ej, envir = levelsenv) : object 'GSM151013' not found
Any help would be appreciated
You might have syntax problem.
Try this:
design <- c('GSM151013', 'GSM150949', 'GSM151014', 'GSM150950',
'GSM151016', 'GSM150951', 'GSM151019', 'GSM150953',
'GSM151025', 'GSM150954')
makeContrasts(GSM151013-GSM150949, GSM151014-GSM150950, GSM151016-GSM150951,
GSM151019-GSM150953, GSM151025-GSM150954, levels=design)
I am trying to train a model using bstTree method and print out the confusion matrix. adverse_effects is my class attribute.
set.seed(1234)
splitIndex <- createDataPartition(attended_num_new_bstTree$adverse_effects, p = .80, list = FALSE, times = 1)
trainSplit <- attended_num_new_bstTree[ splitIndex,]
testSplit <- attended_num_new_bstTree[-splitIndex,]
ctrl <- trainControl(method = "cv", number = 5)
model_bstTree <- train(adverse_effects ~ ., data = trainSplit, method = "bstTree", trControl = ctrl)
predictors <- names(trainSplit)[names(trainSplit) != 'adverse_effects']
pred_bstTree <- predict(model_bstTree$finalModel, testSplit[,predictors])
plot.roc(auc_bstTree)
conf_bstTree= confusionMatrix(pred_bstTree,testSplit$adverse_effects)
But I get the error 'Error in confusionMatrix.default(pred_bstTree, testSplit$adverse_effects) :
The data must contain some levels that overlap the reference.'
max(pred_bstTree)
[1] 1.03385
min(pred_bstTree)
[1] 1.011738
> unique(trainSplit$adverse_effects)
[1] 0 1
Levels: 0 1
How can I fix this issue?
> head(trainSplit)
type New_missed Therapytypename New_Diesease gender adverse_effects change_in_exposure other_reasons other_medication
5 2 1 14 13 2 0 0 0 0
7 2 0 14 13 2 0 0 0 0
8 2 0 14 13 2 0 0 0 0
9 2 0 14 13 2 1 0 0 0
11 2 1 14 13 2 0 0 0 0
12 2 0 14 13 2 0 0 0 0
uvb_puva_type missed_prev_dose skintypeA skintypeB Age DoseB DoseA
5 5 1 1 1 22 3.000 0
7 5 0 1 1 22 4.320 0
8 5 0 1 1 22 4.752 0
9 5 0 1 1 22 5.000 0
11 5 1 1 1 22 5.000 0
12 5 0 1 1 22 5.000 0
I had similar problem, which refers to this error. I used function confusionMatrix:
confusionMatrix(actual, predicted, cutoff = 0.5)
An I got the following error: Error in confusionMatrix.default(actual, predicted, cutoff = 0.5) : The data must contain some levels that overlap the reference.
I checked couple of things like:
class(actual) -> numeric
class(predicted) -> integer
unique(actual) -> plenty values, since it is probability
unique(predicted) -> 2 levels: 0 and 1
I concluded, that there is problem with applying cutoff part of the function, so I did it before by:
predicted<-ifelse(predicted> 0.5,1,0)
and run the confusionMatrix function, which works now just fine:
cm<- confusionMatrix(actual, predicted)
cm$table
which generated correct outcome.
One takeaway for your case, which might improve interpretation once you make code working:
you mixed input values for your confusion matrix(as per confusionMatrix package documetation), instead of:
conf_bstTree= confusionMatrix(pred_bstTree,testSplit$adverse_effects)
you should have written:
conf_bstTree= confusionMatrix(testSplit$adverse_effects,pred_bstTree)
As said it will most likely help you interpret confusion matrix, once you figure out way to make it work.
Hope it helps.
max(pred_bstTree) [1] 1.03385
min(pred_bstTree) [1] 1.011738
and errors tells it all. Plotting ROC is simply checking the effect of different threshold points. Based on threshold rounding happens e.g. 0.7 will be converted to 1 (TRUE class) and 0.3 will be go 0 (FALSE class); in case threshold is 0.5. Threshold values are in range of (0,1)
In your case regardless of threshold you will always get all observations into TRUE class as even minimum prediction is greater than 1. (Thats why #phiver was wondering if you are doing regression instead of classification) . Without any zero in prediction there is no level in 'prediction' which coincide with zero level in adverse_effects and hence this error.
PS: It will be difficult to tell root cause of error without you posting your data
When do text mining using R, after reprocessing text data, we need create a document-term matrix for further exploring. But in similar with Chinese, English also have some certain phases, such as "semantic distance", "machine learning", if you segment them into word, it have totally different meanings, I want to know how to segment document into phases but not word(term).
You can do this in R using the quanteda package, which can detect multi-word expressions as statistical collocates, which would be the multi-word expressions that you are probably referring to in English. To remove the collocations containing stop words, you would first tokenise the text, then remove the stop words leaving a "pad" in place to prevent false adjacencies in the results (two words that were not actually adjacent before the removal of stop words between them).
require(quanteda)
pres_tokens <-
tokens(data_corpus_inaugural) %>%
tokens_remove("\\p{P}", padding = TRUE, valuetype = "regex") %>%
tokens_remove(stopwords("english"), padding = TRUE)
pres_collocations <- textstat_collocations(pres_tokens, size = 2)
head(pres_collocations)
# collocation count count_nested length lambda z
# 1 united states 157 0 2 7.893307 41.19459
# 2 let us 97 0 2 6.291128 36.15520
# 3 fellow citizens 78 0 2 7.963336 32.93813
# 4 american people 40 0 2 4.426552 23.45052
# 5 years ago 26 0 2 7.896626 23.26935
# 6 federal government 32 0 2 5.312702 21.80328
# convert the corpus collocations into single tokens, for top 1,500 collocations
pres_compounded_tokens <- tokens_compound(pres_tokens, pres_collocations[1:1500])
tokens_select(pres_compounded_tokens[2], "*_*")
# tokens from 1 document.
# 1793-Washington :
# [1] "called_upon" "shall_endeavor" "high_sense" "official_act"
Using this "compounded" token set, we can now turn this into a document-feature matrix where the features consist of a mixture of original terms (those not found in a collocation) and the collocations. As can be seen below, "united" occurs alone and as part of the collocation "united_states".
pres_dfm <- dfm(pres_compounded_tokens)
head(pres_dfm[1:5, grep("united|states", featnames(pres_dfm))])
# Document-feature matrix of: 5 documents, 10 features (86% sparse).
# 5 x 10 sparse Matrix of class "dfm"
# features
# docs united states statesmen statesmanship reunited unitedly devastates statesman confederated_states united_action
# 1789-Washington 4 2 0 0 0 0 0 0 0 0
# 1793-Washington 1 0 0 0 0 0 0 0 0 0
# 1797-Adams 3 9 0 0 0 0 0 0 0 0
# 1801-Jefferson 0 0 0 0 0 0 0 0 0 0
# 1805-Jefferson 1 4 0 0 0 0 0 0 0 0
If you want a more brute-force approach, it's possible simply to create a document-by-bigram matrix this way:
# just form all bigrams
head(dfm(data_inaugural_corpus, ngrams = 2))
## Document-feature matrix of: 57 documents, 63,866 features.
## (showing first 6 documents and first 6 features)
## features
## docs fellow-citizens_of of_the the_senate senate_and and_of the_house
## 1789-Washington 1 20 1 1 2 2
## 1797-Adams 0 29 0 0 2 0
## 1793-Washington 0 4 0 0 1 0
## 1801-Jefferson 0 28 0 0 3 0
## 1805-Jefferson 0 17 0 0 1 0
## 1809-Madison 0 20 0 0 2 0